






'''simple docstring'''





import os
import shutil
import tempfile
import unittest

import numpy as np

from transformers import AutoTokenizer, BarkProcessor
from transformers.testing_utils import require_torch, slow


@require_torch
class  snake_case__      (					unittest.TestCase):




					def 		A      (  self  :		int      )    ->							int:
								UpperCAmelCase_      :       Dict          =       '''ylacombe/bark-small'''
								UpperCAmelCase_      :       List[str]          =       tempfile.mkdtemp()
								UpperCAmelCase_      :       int          =       '''en_speaker_1'''
								UpperCAmelCase_      :       Union[str, Any]          =       '''This is a test string'''
								UpperCAmelCase_      :       Any          =       '''speaker_embeddings_path.json'''
								UpperCAmelCase_      :       Optional[int]          =       '''speaker_embeddings'''




					def 		A      (  self  :		List[Any]				, **_A  :		Optional[Any]      )    ->							Union[str, Any]:
								return AutoTokenizer.from_pretrained(self.checkpoint				, **_A      )




					def 		A      (  self  :		List[Any]      )    ->							Dict:
								shutil.rmtree(self.tmpdirname      )




					def 		A      (  self  :		List[str]      )    ->							Dict:
								UpperCAmelCase_      :       str          =       self.get_tokenizer()

								UpperCAmelCase_      :       Dict          =       BarkProcessor(tokenizer=_A      )

								processor.save_pretrained(self.tmpdirname      )
								UpperCAmelCase_      :       Tuple          =       BarkProcessor.from_pretrained(self.tmpdirname      )

								self.assertEqual(processor.tokenizer.get_vocab()				, tokenizer.get_vocab()      )




					@slow
					def 		A      (  self  :		Tuple      )    ->							int:
								UpperCAmelCase_      :       List[Any]          =       BarkProcessor.from_pretrained(
								    pretrained_processor_name_or_path=self.checkpoint				, speaker_embeddings_dict_path=self.speaker_embeddings_dict_path				, )
								processor.save_pretrained(
								    self.tmpdirname				, speaker_embeddings_dict_path=self.speaker_embeddings_dict_path				, speaker_embeddings_directory=self.speaker_embeddings_directory				, )

								UpperCAmelCase_      :       List[str]          =       self.get_tokenizer(bos_token='''(BOS)'''				, eos_token='''(EOS)'''      )

								UpperCAmelCase_      :       List[Any]          =       BarkProcessor.from_pretrained(
								    self.tmpdirname				, self.speaker_embeddings_dict_path				, bos_token='''(BOS)'''				, eos_token='''(EOS)'''				, )

								self.assertEqual(processor.tokenizer.get_vocab()				, tokenizer_add_kwargs.get_vocab()      )




					def 		A      (  self  :		str      )    ->							List[str]:
								UpperCAmelCase_      :       Union[str, Any]          =       BarkProcessor.from_pretrained(
								    pretrained_processor_name_or_path=self.checkpoint				, speaker_embeddings_dict_path=self.speaker_embeddings_dict_path				, )

								UpperCAmelCase_      :       Union[str, Any]          =       35
								UpperCAmelCase_      :       Any          =       2
								UpperCAmelCase_      :       Optional[int]          =       8

								UpperCAmelCase_      :       Tuple          =       {
								    '''semantic_prompt''': np.ones(_A      ),
								    '''coarse_prompt''': np.ones((nb_codebooks_coarse, seq_len)      ),
								    '''fine_prompt''': np.ones((nb_codebooks_total, seq_len)      ),
								}

								# test providing already loaded voice_preset
								UpperCAmelCase_      :       List[str]          =       processor(text=self.input_string				, voice_preset=_A      )

								UpperCAmelCase_      :       Union[str, Any]          =       inputs['''history_prompt''']
								for key in voice_preset:
											self.assertListEqual(voice_preset[key].tolist()				, processed_voice_preset.get(_A				, np.array([]      )      ).tolist()      )

								# test loading voice preset from npz file
								UpperCAmelCase_      :       List[Any]          =       os.path.join(self.tmpdirname				, '''file.npz'''      )
								np.savez(_A				, **_A      )
								UpperCAmelCase_      :       int          =       processor(text=self.input_string				, voice_preset=_A      )
								UpperCAmelCase_      :       List[str]          =       inputs['''history_prompt''']

								for key in voice_preset:
											self.assertListEqual(voice_preset[key].tolist()				, processed_voice_preset.get(_A				, np.array([]      )      ).tolist()      )

								# test loading voice preset from the hub
								UpperCAmelCase_      :       List[str]          =       processor(text=self.input_string				, voice_preset=self.voice_preset      )





					def 		A      (  self  :		List[str]      )    ->							List[Any]:
								UpperCAmelCase_      :       Optional[Any]          =       self.get_tokenizer()

								UpperCAmelCase_      :       Optional[int]          =       BarkProcessor(tokenizer=_A      )

								UpperCAmelCase_      :       str          =       processor(text=self.input_string      )

								UpperCAmelCase_      :       Dict          =       tokenizer(
								    self.input_string				, padding='''max_length'''				, max_length=2_56				, add_special_tokens=_A				, return_attention_mask=_A				, return_token_type_ids=_A				, )

								for key in encoded_tok.keys():
											self.assertListEqual(encoded_tok[key]				, encoded_processor[key].squeeze().tolist()      )










'''simple docstring'''





import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_torch


if is_torch_available():
						import torch

						from transformers.generation import DisjunctiveConstraint


@require_torch
class  snake_case__      (					unittest.TestCase):




					def 		A      (  self  :		Union[str, Any]      )    ->							List[str]:
								# For consistency across different places the DisjunctiveConstraint is called,
								# dc.token_ids is a list of integers. It is also initialized only by integers.

								UpperCAmelCase_      :       Union[str, Any]          =       [[1, 2, 4], [1, 2, 3, 4]]
								UpperCAmelCase_      :       Dict          =       DisjunctiveConstraint(_A      )
								self.assertTrue(isinstance(dc.token_ids				, _A      )      )

								with self.assertRaises(_A      ):
											DisjunctiveConstraint(torch.LongTensor([[1, 2, 4], [1, 2, 3]]      )      )

								with self.assertRaises(_A      ):
											DisjunctiveConstraint([torch.LongTensor([1, 2, 4]      ), torch.LongTensor([1, 2, 3, 4, 5]      )]      )




					def 		A      (  self  :		Optional[int]      )    ->							List[Any]:
								# We can't have constraints that are complete subsets of another. This leads to a preverse
								# interpretation of "constraint fulfillment": does generating [1,2,3] fulfill the constraint?
								# It would mean that it generated [1,2] which fulfills it, but it's in the middle of potentially
								# fulfilling [1,2,3,4]. If we believe that [1,2,3] does fulfill the constraint, then the algorithm
								# will necessarily never reach [1,2,3,4], giving users a false sense of control (better to just not allow it).
								UpperCAmelCase_      :       Optional[Any]          =       [[1, 2], [1, 2, 3, 4]]

								with self.assertRaises(_A      ):
											DisjunctiveConstraint(_A      )  # fails here




					def 		A      (  self  :		List[str]      )    ->							int:
								UpperCAmelCase_      :       Dict          =       [[1, 2, 3], [1, 2, 4]]

								UpperCAmelCase_      :       List[Any]          =       DisjunctiveConstraint(_A      )

								UpperCAmelCase_    ,      UpperCAmelCase_    ,      UpperCAmelCase_      :       List[str]          =       dc.update(1      )
								UpperCAmelCase_      :       int          =       stepped is True and completed is False and reset is False
								self.assertTrue(_A      )
								self.assertTrue(not dc.completed      )
								self.assertTrue(dc.current_seq == [1]      )

								UpperCAmelCase_    ,      UpperCAmelCase_    ,      UpperCAmelCase_      :       Tuple          =       dc.update(2      )
								UpperCAmelCase_      :       Optional[int]          =       stepped is True and completed is False and reset is False
								self.assertTrue(_A      )
								self.assertTrue(not dc.completed      )
								self.assertTrue(dc.current_seq == [1, 2]      )

								UpperCAmelCase_    ,      UpperCAmelCase_    ,      UpperCAmelCase_      :       Any          =       dc.update(3      )
								UpperCAmelCase_      :       List[Any]          =       stepped is True and completed is True and reset is False
								self.assertTrue(_A      )
								self.assertTrue(dc.completed      )  # Completed!
								self.assertTrue(dc.current_seq == [1, 2, 3]      )





					def 		A      (  self  :		List[Any]      )    ->							Any:
								UpperCAmelCase_      :       int          =       [[1, 2, 3], [1, 2, 4, 5], [1, 2, 5]]

								UpperCAmelCase_      :       str          =       DisjunctiveConstraint(_A      )

								UpperCAmelCase_    ,      UpperCAmelCase_    ,      UpperCAmelCase_      :       Tuple          =       dc.update(1      )
								self.assertTrue(not dc.completed      )
								self.assertTrue(dc.current_seq == [1]      )

								UpperCAmelCase_    ,      UpperCAmelCase_    ,      UpperCAmelCase_      :       str          =       dc.update(2      )
								self.assertTrue(not dc.completed      )
								self.assertTrue(dc.current_seq == [1, 2]      )

								UpperCAmelCase_    ,      UpperCAmelCase_    ,      UpperCAmelCase_      :       str          =       dc.update(4      )
								self.assertTrue(not dc.completed      )
								self.assertTrue(dc.current_seq == [1, 2, 4]      )

								UpperCAmelCase_    ,      UpperCAmelCase_    ,      UpperCAmelCase_      :       Any          =       dc.update(5      )
								self.assertTrue(dc.completed      )  # Completed!
								self.assertTrue(dc.current_seq == [1, 2, 4, 5]      )

								dc.reset()

								UpperCAmelCase_    ,      UpperCAmelCase_    ,      UpperCAmelCase_      :       List[str]          =       dc.update(1      )
								self.assertTrue(not dc.completed      )
								self.assertTrue(dc.remaining() == 3      )
								self.assertTrue(dc.current_seq == [1]      )

								UpperCAmelCase_    ,      UpperCAmelCase_    ,      UpperCAmelCase_      :       Optional[int]          =       dc.update(2      )
								self.assertTrue(not dc.completed      )
								self.assertTrue(dc.remaining() == 2      )
								self.assertTrue(dc.current_seq == [1, 2]      )

								UpperCAmelCase_    ,      UpperCAmelCase_    ,      UpperCAmelCase_      :       int          =       dc.update(5      )
								self.assertTrue(dc.completed      )  # Completed!
								self.assertTrue(dc.remaining() == 0      )
								self.assertTrue(dc.current_seq == [1, 2, 5]      )


